Adaptive Method for Machine Learning Model Selection in Data Science Projects.

Data science projects involve a machine learning (ML) process based on data, code, and models that change over time. For example, the datasets may increase in size and allow an ML model that requires larger datasets to be applied. However, the dynamic factors that influence model selection are not well understood and explicitly represented. This paper presents ongoing work on an adaptive method for ML model selection in big data science projects. The proposed method involves (i) identifying the factors that affect model selection based on heuristics proposed in the literature; and (ii) modeling the variability of these factors using a feature diagram and constraints that trigger adaptive reconfiguration, that is, changes in model selection due to changes in the variability factors. The applicability of the method is demonstrated through an illustrative use case. The proposed method can lead to an improved understanding of dynamic factors that influence model selection, how these factors explicitly affect the selection, and how the adaptive factors can be represented and automated. This improved understanding can result in a project model selection process that is less implicit and more efficient, more adaptive and explainable, and ultimately constitute a foundation for the creation of novel dynamic software product lines to support this process.